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Artificial Intelligence for Trading

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Artificial Intelligence for Trading provided by Udacity is a comprehensive online course, which lasts for 26 weeks long, 10 hours a week. Artificial Intelligence for Trading is taught by Cindy Lin, Arpan Chakraborty, Elizabeth Otto Hamel, Eddy Shyu, Brok Bucholtz, Parnian Barekatain, Juan Delgado, Luis Serrano, Cezanne Camacho, Mat Leonard, Eduardo P., Hsin-Wen C., Frank Salvador Y., Sridhar S., Jonnie W. and André V.. Upon completion of the course, you can receive an e-certificate from Udacity. The course is taught in Englishand is Paid Course. Visit the course page at Udacity for detailed price information.

WorldQuant via Udacity
26 weeks long, 10 hours a week
21st, Oct 2022
Overview

  • In this program, you’ll analyze real data and build financial models for trading. Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in quantitative finance, this program offers you the opportunity to master valuable data and AI skills.
    Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.

Syllabus

    • Basic Quantitative Trading
      • Learn about market mechanics and how to generate signals with stock data. Work on developing a momentum-trading strategy in your first project.
    • Advanced Quantitative Trading
      • Learn the quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading.
    • Stocks, Indices, and ETFs
      • Learn about portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs.
    • Factor Investing and Alpha Research
      • Learn about alpha and risk factors, and construct a portfolio with advanced optimization techniques.
    • Sentiment Analysis with Natural Language Processing
      • Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.
    • Advanced Natural Language Processing with Deep Learning
      • Learn to apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals.
    • Combining Multiple Signals
      • Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.
    • Simulating Trades with Historical Data
      • Learn to refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells.

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4, based on 1 Course votes at GetYourEducation

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